def Math_Transform(dataframe):
#Math Transform Functions
#ACOS - Vector Trigonometric ACos
df[f'{ratio}_ACOS'] = talib.ACOS(Close)
#ASIN - Vector Trigonometric ASin
df[f'{ratio}_ASIN'] = talib.ASIN(Close)
#ATAN - Vector Trigonometric ATan
df[f'{ratio}_ATAN'] = talib.ATAN(Close)
#CEIL - Vector Ceil
df[f'{ratio}_CEIL'] = talib.CEIL(Close)
#COS - Vector Trigonometric Cos
df[f'{ratio}_COS'] = talib.COS(Close)
#COSH - Vector Trigonometric Cosh
df[f'{ratio}_COSH'] = talib.COSH(Close)
#EXP - Vector Arithmetic Exp
df[f'{ratio}_EXP'] = talib.EXP(Close)
#FLOOR - Vector Floor
df[f'{ratio}_FLOOR'] = talib.FLOOR(Close)
#LN - Vector Log Natural
df[f'{ratio}_LN'] = talib.LN(Close)
#LOG10 - Vector Log10
df[f'{ratio}_LOG10'] = talib.LOG10(Close)
#SIN - Vector Trigonometric Sin
df[f'{ratio}_SIN'] = talib.SIN(Close)
#SINH - Vector Trigonometric Sinh
df[f'{ratio}_SINH'] = talib.SINH(Close)
#SQRT - Vector Square Root
df[f'{ratio}_SQRT'] = talib.SQRT(Close)
#TAN - Vector Trigonometric Tan
df[f'{ratio}_TAN'] = talib.TAN(Close)
#TANH - Vector Trigonometric Tanh
df[f'{ratio}_TANH'] = talib.TANH(Close)
return
def math_transform_process(event):
print(event.widget.get())
math_transform = event.widget.get()
upperband, middleband, lowerband = ta.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)
fig, axes = plt.subplots(2, 1, sharex=True)
ax1, ax2 = axes[0], axes[1]
axes[0].plot(close, 'rd-', markersize=3)
axes[0].plot(upperband, 'y-')
axes[0].plot(middleband, 'b-')
axes[0].plot(lowerband, 'y-')
axes[0].set_title(math_transform, fontproperties="SimHei")
if math_transform == '反余弦':
real = ta.ACOS(close)
axes[1].plot(real, 'r-')
elif math_transform == '反正弦':
real = ta.ASIN(close)
axes[1].plot(real, 'r-')
elif math_transform == '反正切':
real = ta.ATAN(close)
axes[1].plot(real, 'r-')
elif math_transform == '向上取整':
real = ta.CEIL(close)
axes[1].plot(real, 'r-')
elif math_transform == '余弦':
real = ta.COS(close)
axes[1].plot(real, 'r-')
elif math_transform == '双曲余弦':
real = ta.COSH(close)
axes[1].plot(real, 'r-')
elif math_transform == '指数':
real = ta.EXP(close)
axes[1].plot(real, 'r-')
elif math_transform == '向下取整':
real = ta.FLOOR(close)
axes[1].plot(real, 'r-')
elif math_transform == '自然对数':
real = ta.LN(close)
axes[1].plot(real, 'r-')
elif math_transform == '常用对数':
real = ta.LOG10(close)
axes[1].plot(real, 'r-')
elif math_transform == '正弦':
real = ta.SIN(close)
axes[1].plot(real, 'r-')
elif math_transform == '双曲正弦':
real = ta.SINH(close)
axes[1].plot(real, 'r-')
elif math_transform == '平方根':
real = ta.SQRT(close)
axes[1].plot(real, 'r-')
elif math_transform == '正切':
real = ta.TAN(close)
axes[1].plot(real, 'r-')
elif math_transform == '双曲正切':
real = ta.TANH(close)
axes[1].plot(real, 'r-')
plt.show()
def genTA(data, y, t): #t is timeperiod
indicators = {}
y_ind = copy.deepcopy(y)
for ticker in data:
## Overlap
indicators[ticker] = ta.SMA(data[ticker].iloc[:,3], timeperiod=t).to_frame()
indicators[ticker]['EMA'] = ta.EMA(data[ticker].iloc[:,3], timeperiod=t)
indicators[ticker]['BBAND_Upper'], indicators[ticker]['BBAND_Middle' ], indicators[ticker]['BBAND_Lower' ] = ta.BBANDS(data[ticker].iloc[:,3], timeperiod=t, nbdevup=2, nbdevdn=2, matype=0)
indicators[ticker]['HT_TRENDLINE'] = ta.HT_TRENDLINE(data[ticker].iloc[:,3])
indicators[ticker]['SAR'] = ta.SAR(data[ticker].iloc[:,1], data[ticker].iloc[:,2], acceleration=0, maximum=0)
#rename SMA column
indicators[ticker].rename(columns={indicators[ticker].columns[0]: "SMA"}, inplace=True)
## Momentum
indicators[ticker]['RSI'] = ta.RSI(data[ticker].iloc[:,3], timeperiod=(t-1))
indicators[ticker]['MOM'] = ta.MOM(data[ticker].iloc[:,3], timeperiod=(t-1))
indicators[ticker]['ROC'] = ta.ROC(data[ticker].iloc[:,3], timeperiod=(t-1))
indicators[ticker]['ROCP']= ta.ROCP(data[ticker].iloc[:,3],timeperiod=(t-1))
indicators[ticker]['STOCH_SLOWK'], indicators[ticker]['STOCH_SLOWD'] = ta.STOCH(data[ticker].iloc[:,1], data[ticker].iloc[:,2], data[ticker].iloc[:,3], fastk_period=t, slowk_period=int(.6*t), slowk_matype=0, slowd_period=int(.6*t), slowd_matype=0)
indicators[ticker]['MACD'], indicators[ticker]['MACDSIGNAL'], indicators[ticker]['MACDHIST'] = ta.MACD(data[ticker].iloc[:,3], fastperiod=t,slowperiod=2*t,signalperiod=int(.7*t))
## Volume
indicators[ticker]['OBV'] = ta.OBV(data[ticker].iloc[:,3], data[ticker].iloc[:,4])
indicators[ticker]['AD'] = ta.AD(data[ticker].iloc[:,1], data[ticker].iloc[:,2], data[ticker].iloc[:,3], data[ticker].iloc[:,4])
indicators[ticker]['ADOSC'] = ta.ADOSC(data[ticker].iloc[:,1], data[ticker].iloc[:,2], data[ticker].iloc[:,3], data[ticker].iloc[:,4], fastperiod=int(.3*t), slowperiod=t)
## Cycle
indicators[ticker]['HT_DCPERIOD'] = ta.HT_DCPERIOD(data[ticker].iloc[:,3])
indicators[ticker]['HT_TRENDMODE']= ta.HT_TRENDMODE(data[ticker].iloc[:,3])
## Price
indicators[ticker]['AVGPRICE'] = ta.AVGPRICE(data[ticker].iloc[:,0], data[ticker].iloc[:,1], data[ticker].iloc[:,2], data[ticker].iloc[:,3])
indicators[ticker]['TYPPRICE'] = ta.TYPPRICE(data[ticker].iloc[:,1], data[ticker].iloc[:,2], data[ticker].iloc[:,3])
## Volatility
indicators[ticker]['ATR'] = ta.ATR(data[ticker].iloc[:,1], data[ticker].iloc[:,2], data[ticker].iloc[:,3], timeperiod=(t-1))
## Statistics
indicators[ticker]['BETA'] = ta.BETA(data[ticker].iloc[:,1], data[ticker].iloc[:,2], timeperiod=(t-1))
indicators[ticker]['LINEARREG'] = ta.LINEARREG(data[ticker].iloc[:,3], timeperiod=t)
indicators[ticker]['VAR'] = ta.VAR(data[ticker].iloc[:,3], timeperiod=t, nbdev=1)
## Math Transform
indicators[ticker]['EXP'] = ta.EXP(data[ticker].iloc[:,3])
indicators[ticker]['LN'] = ta.LN(data[ticker].iloc[:,3])
## Patterns (returns integers - but norming might not really do anything but wondering if they should be normed)
indicators[ticker]['CDLENGULFING'] = ta.CDLENGULFING(data[ticker].iloc[:,0], data[ticker].iloc[:,1], data[ticker].iloc[:,2], data[ticker].iloc[:,3])
indicators[ticker]['CDLDOJI'] = ta.CDLDOJI(data[ticker].iloc[:,0], data[ticker].iloc[:,1], data[ticker].iloc[:,2], data[ticker].iloc[:,3])
indicators[ticker]['CDLHAMMER'] = ta.CDLHAMMER(data[ticker].iloc[:,0], data[ticker].iloc[:,1], data[ticker].iloc[:,2], data[ticker].iloc[:,3])
indicators[ticker]['CDLHANGINGMAN']= ta.CDLHANGINGMAN(data[ticker].iloc[:,0], data[ticker].iloc[:,1], data[ticker].iloc[:,2], data[ticker].iloc[:,3])
#drop 'nan' values
indicators[ticker].drop(indicators[ticker].index[np.arange(0,63)], inplace=True)
y_ind[ticker].drop(y_ind[ticker].index[np.arange(0,63)], inplace=True)
#Normalize Features
indicators_norm = normData(indicators)
return indicators_norm, indicators, y_ind
def vol(self, n, array=False):
"""波动率指标"""
logrtn = talib.LN(self.high / self.low)
stdrtn = talib.STDDEV(logrtn, n)
vol = talib.EXP(stdrtn) - 1
if array:
return vol
return vol[-1]
def EXP(close):
''' Vector Arithmetic Exp 指数曲线，三角函数
分组: Math Transform 数学变换
简介:
real = EXP(close)
'''
return talib.EXP(close)
def exp(client, symbol, timeframe="6m", col="close"):
"""This will return a dataframe of
Vector Arithmetic Exp
for the given symbol across the given timeframe
Args:
client (pyEX.Client); Client
symbol (string); Ticker
timeframe (string); timeframe to use, for pyEX.chart
col (string); column to use to calculate
Returns:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
x = t.EXP(df[col].values)
return pd.DataFrame({col: df[col].values, "exp": x})
def EXP(data, **kwargs):
_check_talib_presence()
prices = _extract_series(data)
return talib.EXP(prices, **kwargs)
def main():
# read csv file and transform it to datafeed (df):
df = pd.read_csv(current_dir + "/" + base_dir + "/" + in_dir + "/" +
in_dir + '_' + stock_symbol + '.csv')
# set numpy datafeed from df:
df_numpy = {
'Date': np.array(df['date']),
'Open': np.array(df['open'], dtype='float'),
'High': np.array(df['high'], dtype='float'),
'Low': np.array(df['low'], dtype='float'),
'Close': np.array(df['close'], dtype='float'),
'Volume': np.array(df['volume'], dtype='float')
}
date = df_numpy['Date']
openp = df_numpy['Open']
high = df_numpy['High']
low = df_numpy['Low']
close = df_numpy['Close']
volume = df_numpy['Volume']
#########################################
##### Math Transform Functions ######
#########################################
#ACOS - Vector Trigonometric ACos
acos = ta.ACOS(close)
#ASIN - Vector Trigonometric ASin
asin = ta.ASIN(close)
#ATAN - Vector Trigonometric ATan
atan = ta.ATAN(close)
#CEIL - Vector Ceil
ceil = ta.CEIL(close)
#COS - Vector Trigonometric Cos
cos = ta.COS(close)
#COSH - Vector Trigonometric Cosh
cosh = ta.COSH(close)
#EXP - Vector Arithmetic Exp
exp = ta.EXP(close)
#FLOOR - Vector Floor
floor = ta.FLOOR(close)
#LN - Vector Log Natural
ln = ta.LN(close)
#LOG10 - Vector Log10
log10 = ta.LOG10(close)
#SIN - Vector Trigonometric Sin
sin = ta.SIN(close)
#SINH - Vector Trigonometric Sinh
sinh = ta.SINH(close)
#SQRT - Vector Square Root
sqrt = ta.SQRT(close)
#TAN - Vector Trigonometric Tan
tan = ta.TAN(close)
#TANH - Vector Trigonometric Tanh
tanh = ta.TANH(close)
df_save = pd.DataFrame(
data={
'date': np.array(df['date']),
'acos': acos,
'asin': asin,
'atan': atan,
'ceil': ceil,
'cos': cos,
'cosh': cosh,
'exp': exp,
'floor': floor,
'ln': ln,
'log10': log10,
'sin': sin,
'sinh': sinh,
'sqrt': sqrt,
'tan': tan,
'tanh': tanh
})
df_save.to_csv(current_dir + "/" + base_dir + "/" + out_dir + '/' +
stock_symbol + "/" + out_dir + '_ta_math_transform_' +
stock_symbol + '.csv',
index=False)
def EXP(Close):
return Close.apply(lambda col: ta.EXP(col), axis=0)
def EXP(self, name, **parameters):
data = self.__data[name]
return talib.EXP(data, **parameters)
